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Symmetry
Special Issues
Applications Based on Symmetry in Image Processing and Optimization
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Applications Based on Symmetry in Image Processing and Optimization

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 708

Special Issue Editors

Dr. Longfei Ren

E-Mail Website
Guest Editor
Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: hyperspectral unmixing; optimization; image processing
Dr. Minghua Wang

E-Mail Website
Guest Editor
Institute of Robotics and Automatic Information System (IRAIS) and the Tianjin Key Laboratory of Intelligent Robotic (tjKLIR), Nankai University, Tianjin, China
Interests: remote sensing; image processing; noise removal; anomaly detection; machine learning; deep learning
Dr. Jing Yao

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: hyperspectral imaging; mutlimodal data fusion; remote sensing; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue on "Applications Based on Symmetry in Image Processing and Optimization" explores the crucial role of symmetry in enhancing image analysis and optimization tasks. Symmetry in deep learning and machine learning is a powerful tool in image processing, particularly in areas such as remote sensing imaging, where it aids in improving the accuracy and efficiency of tasks like image segmentation, reconstruction, and denoising. Researchers can design algorithms that more effectively handle complex image data, such as in remote sensing, MRI, X-ray, PET, and ultrasound imaging.

Symmetry-based approaches help maintain structural integrity in images while reducing noise and improving resolution, which is especially important in diagnostic imaging. This issue highlights advancements in symmetry-based methodologies that contribute to the development of innovative techniques for image enhancement and analysis. Furthermore, the application of symmetry in optimization algorithms allows for more efficient computational processes, which is vital for processing large-scale imaging datasets. This Special Issue invites research that bridges the gap between theoretical symmetry principles and practical applications in image processing, aiming to drive forward the capabilities and accuracy of medical imaging and other imaging modalities.

Dr. Longfei Ren
Dr. Minghua Wang
Dr. Jing Yao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing images
  • image processing
  • medical imaging
  • image reconstruction
  • image denoising
  • MRI
  • X-ray imaging
  • PET imaging
  • ultrasound imaging
  • computational efficiency
  • applications of symmetry in different image modalities

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Published Papers (1 paper)

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Research

20 pages, 3535 KiB  
Article
L2,1-Norm Regularized Double Non-Negative Matrix Factorization for Hyperspectral Change Detection
by Xing-Hui Zhu, Meng-Ting Li, Yang-Jun Deng, Xu Luo, Lu-Ming Shen and Chen-Feng Long
Symmetry 2025, 17(2), 304; https://doi.org/10.3390/sym17020304 - 17 Feb 2025
Viewed by 505
Abstract
Hyperspectral image (HSI) change detection (CD) is an important technology for identifying surface changes using multi-temporal HSIs. Nevertheless, the high dimensionality of HSIs presents significant challenges for CD tasks, including issues such as lack of robustness and high computational costs in existing methods. [...] Read more.
Hyperspectral image (HSI) change detection (CD) is an important technology for identifying surface changes using multi-temporal HSIs. Nevertheless, the high dimensionality of HSIs presents significant challenges for CD tasks, including issues such as lack of robustness and high computational costs in existing methods. To address those issues, this paper proposes an unsupervised simple and effective HSI CD model termed L2,1-norm regularized double non-negative matrix factorization (L2,1-DNMF). Specifically, the proposed model employs a symmetric double non-negative matrix factorization (NMF) framework to jointly analyze multitemporal HSIs, capturing their common and invariant structural information to construct a shared feature basis. Meanwhile, two non-negative feature weight matrices are learned to generate a differential image matrix that directly reflects the change regions. To enhance robustness against noise, an L2,1-norm constraint is imposed on the difference image matrix, ensuring that unchanged areas exhibit near-zero values while changed areas present nonzero values. Finally, comprehensive experiments performed on three benchmark hyperspectral datasets validated the efficacy of the proposed method, which is superior to some state-of-the-art ones regarding detection performance and computational cost. Full article
(This article belongs to the Special Issue Applications Based on Symmetry in Image Processing and Optimization)
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